Strong, but not weak, noise correlations are beneficial for population coding

Abstract

Neural correlations play a critical role in sensory information coding. They are of two kinds: signal correlations, when neurons have overlapping sensitivities, and noise correlations from network effects and shared noise. In experiments from early sensory systems and cortex, many pairs of neurons typically show both types of correlations to be positive and large, especially between nearby neurons with similar stimulus sensitivity. However, theoretical arguments have suggested that stimulus and noise correlations should have opposite signs to improve coding, at odds with experimental observations. We analyze retinal recording in response to a large variety of stimuli, and show that, contrary to common belief, large noise correlations are beneficial for coding, even if aligned with signal correlations. To understand this result, we develop a theory of visual information coding by correlated neurons, which resolves that paradox. We show that noise correlations are always beneficial if they are strong enough, unless neurons are perfectly correlated by the stimulus. Finally, using neuronal recordings and modeling, we show that for high dimensional stimuli noise correlation benefits the encoding of fine-grained details of visual stimuli, at the expense of large-scale features, which are already well encoded.